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Creators/Authors contains: "Weston J. Jackson, Ipsita Agarwal"

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  1. (, Journal of healthcare engineering)
    icrobiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster- characterizing ones, and therefore lie in-between them in cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project. 
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    Accepted Manuscript Full Text Available